SUMMARY Emerging adulthood (ages 18-24 years) is marked by substantial weight gain, leading to increased lifetime risks of cancer and other chronic diseases. Engaging in sufficient levels of physical activity and sleep, and limiting sedentary time are important contributors to the prevention of weight gain. However, engaging in these healthy behaviors peaks during the childhood and adolescent years, and steeply deteriorates into emerging adulthood. Interventions promoting physical activity, reduced sedentary time, and sufficient sleep typically focus on the adoption of these behaviors. Yet, when these interventions are successful, new patterns of behavior are not maintained and typically regress back to baseline levels. Traditional health behavior theories provide limited guidance regarding factors underlying behavior maintenance. To address this gap, our work suggests that dual-process models of decision-making and behavior can shed light on differences in the mechanisms underlying adoption versus maintenance. Reflective processes (e.g., efficacy, deliberations, self-control) may be activated to a greater extent during behavior adoption. In contrast, reactive processes (e.g., contextual cues, automaticity, habits) may play a greater role in behavior maintenance. However, reactive processes are difficult to measure using retrospective methods because they can unfold on a micro-timescale (i.e., change across minutes or hours). To solve this problem, we propose to use real-time mobile technologies to collect intensive longitudinal data examining differences in the micro-temporal processes underlying the adoption and maintenance of physical activity, low sedentary time, and sufficient sleep duration. We will conduct a prospective within- subject case-crossover observational study across a 12-month period. Ethnically-diverse, emerging adults (ages 18-24, N=300) will be recruited from the Happiness & Health Cohort (R01DA033296). We will conduct intermittent self-report (i.e., ecological momentary assessment) of reflective variables; and continuous, sensor- based passive monitoring of reactive variables (e.g., location, social proximity, voice/text communication) and behaviors (i.e., physical activity, sedentary time, sleep) using smartwatches and smartphones. These data will be used to predict within-subject variation (within-days, between-days) in the likelihood of behavior ?episodes? (e.g., ?10 min of physical activity, ?120 min sedentary time, ?7 hr sleep) and ?lapses? (i.e., failure to achieve recommended levels ?7 days). The specific aims are to (1) idiographically use machine learning to identify person-specific combinations of time-varying reflective and reactive factors that predict behavior episodes and lapse; and (2) nomothetically determine whether there are general, group-level patterns of time-varying predictors, and whether those patterns predict successful behavior maintenance outcomes. The data and methods from this project will contribute to the U01/U24 Intensive Longitudinal Behavior Initiative?s collective goal to build more predictive health behavior theories that specify targets for personalized interventions.